Insights.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. Data Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from os import listdir\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "from IPython.display import clear_output\n",
    "from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier\n",
    "\n",
    "from PyEMD import EMD\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['charge_Jan_FEB_2017.csv', 'ICD_2017.csv', 'charge_Nov_dec_2017.csv', 'charge_Jul_Aug_2017.csv', 'charge_mar_apr_2017.csv', 'charge_Sep_oct_2017.csv', 'charge_May_Jun_2017.csv'] ['charge_Jan_Feb_Mar_2016.csv', 'DRG 2015-2018 FEB.csv', 'charge_Dec_2016.csv', 'ICD_2015_2016.csv', 'charge_Oct_Nov_Dec_2015.csv', 'charge_Apr_May_Jun_2016.csv', 'charge_Oct_Nov_2016.csv', 'charge_Jan_Feb_March_2015.csv', 'charge_Jul_Aug_Sep_2015.csv', 'charge_Jul_Aug_Sep_2016.csv', 'charge_Apr_May_Jun_2015.csv']\n",
      "charge_Jan_FEB_2017.csv\n",
      "ICD_2017.csv\n",
      "charge_Nov_dec_2017.csv\n",
      "charge_Jul_Aug_2017.csv\n",
      "charge_mar_apr_2017.csv\n",
      "charge_Sep_oct_2017.csv\n",
      "charge_May_Jun_2017.csv\n",
      "charge_Jan_Feb_Mar_2016.csv\n",
      "DRG 2015-2018 FEB.csv\n",
      "charge_Dec_2016.csv\n",
      "ICD_2015_2016.csv\n",
      "charge_Oct_Nov_Dec_2015.csv\n",
      "charge_Apr_May_Jun_2016.csv\n",
      "charge_Oct_Nov_2016.csv\n",
      "charge_Jan_Feb_March_2015.csv\n",
      "charge_Jul_Aug_Sep_2015.csv\n",
      "charge_Jul_Aug_Sep_2016.csv\n",
      "charge_Apr_May_Jun_2015.csv\n",
      "WARNING *** file size (8402215) not 512 + multiple of sector size (512)\n",
      "WARNING *** file size (1385899) not 512 + multiple of sector size (512)\n",
      "WARNING *** file size (2484943) not 512 + multiple of sector size (512)\n",
      "WARNING *** file size (2140214) not 512 + multiple of sector size (512)\n",
      "WARNING *** file size (4508427) not 512 + multiple of sector size (512)\n"
     ]
    }
   ],
   "source": [
    "# t_df = t_df[['FIN','QTY','PRICE','TOTAL_RRVENUE','TRANS_DATE']]\n",
    "\n",
    "path = '/home/hennestech/FIN/'\n",
    "path1 = '/home/hennestech/FIN_2015_2016/Fin 2015_1016/'\n",
    "file_extension = '.csv'\n",
    "\n",
    "\n",
    "file_name_list = [f for f in listdir(path) if f.endswith(file_extension)] \n",
    "file_name_list1 =  [f for f in listdir(path1) if f.endswith(file_extension)]\n",
    "print(file_name_list,file_name_list1)\n",
    "\n",
    "dfs = []\n",
    "\n",
    "for i in range(len(file_name_list)):\n",
    "    print(file_name_list[i])\n",
    "    if i == 0:\n",
    "        t_df = pd.read_csv(path+file_name_list[i],sep=',')\n",
    "    else:\n",
    "        t_df = t_df.append(pd.read_csv(path+file_name_list[i], sep=',',  header = None,names=list(t_df)))\n",
    "            \n",
    "            \n",
    "for i in range(len(file_name_list1)):\n",
    "    print(file_name_list1[i])\n",
    "    if file_name_list1[i]!= 'DRG 2015-2018 FEB.csv':\n",
    "        t_df = t_df.append(pd.read_csv(path1+file_name_list1[i], sep=',',  header = None,names=list(t_df)))\n",
    "        \n",
    "        \n",
    "\n",
    "\n",
    "path = '/home/hennestech/FIN/'\n",
    "path1 = '/home/hennestech/FIN_2015_2016/Fin 2015_1016/'\n",
    "\n",
    "\n",
    "df_enc = pd.read_excel(path+'Encounter_2017.xls')\n",
    "df_enc = df_enc.append(pd.read_excel(path+'Encounter_2017_Jan_FEB.xls'))\n",
    "df_enc = df_enc.append(pd.read_excel(path1+'Encounter_2015_2016.xls'))\n",
    "\n",
    "df_icd = pd.read_csv(path+'ICD_2017.csv')\n",
    "df_icd = df_icd.append(pd.read_excel(path+'ICD_2017_Jan_FEB.xls'))\n",
    "df_icd = df_icd.append(pd.read_csv(path1+'ICD_2015_2016.csv'))\n",
    "\n",
    "\n",
    "\n",
    "path2 = '/home/hennestech/DRG_2Q/'\n",
    "df_enc = df_enc.append(pd.read_excel(path2+'DRG ENC 2st Q 2018.xls'))\n",
    "df_icd = df_icd.append(pd.read_excel(path2+'DRG ICD 2st Q 2018.xls'))\n",
    "t_df =   t_df.append(pd.read_csv(path2+'DRG Charg 2st Q 2018.csv',sep=',', header = None,names=list(t_df)))\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# calculation of length of stay\n",
    "df_enc['length of stay'] = pd.to_datetime(df_enc['DISCHARGE_DATE']) - pd.to_datetime(df_enc['ADMISSION_DATE'])\n",
    "\n",
    "\n",
    "# filter data\n",
    "t_df = t_df[['FIN','QTY','PRICE','TOTAL_RRVENUE','TRANS_DATE']]\n",
    "df_enc = df_enc[['FIN', 'MRN','ENCOUNTER_TYPE','length of stay']]\n",
    "df_icd = df_icd[['FIN','PRIORITY','ICD_CODE']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((15022892, 5), (74428, 4), (273274, 3))"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_df.shape,df_enc.shape,df_icd.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING *** file size (460772) not 512 + multiple of sector size (512)\n"
     ]
    }
   ],
   "source": [
    "path = '/home/hennestech/FIN_2015_2016/Fin 2015_1016/DRG 2015-2018 FEB.csv'# Full Path of DRG file\n",
    "path1 = '/home/hennestech/DRG_2Q/DRG 2st Q 2018.xls'\n",
    "df = pd.read_csv(path)\n",
    "df = df.append(pd.read_excel(path1,usecols=[0,1],names=['DRG','FIN']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Index(['FIN', 'MRN', 'ENCOUNTER_TYPE', 'length of stay'], dtype='object'),\n",
       " Index(['FIN', 'PRIORITY', 'ICD_CODE'], dtype='object'),\n",
       " Index(['FIN', 'QTY', 'PRICE', 'TOTAL_RRVENUE', 'TRANS_DATE'], dtype='object'))"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_enc.columns,df_icd.columns,t_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_enc = pd.merge(df_enc,df,on='FIN')\n",
    "df_icd = pd.merge(df_icd,df,on='FIN')\n",
    "t_df = pd.merge(t_df,df,on='FIN')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "t_df['TRANS_DATE'] = t_df['TRANS_DATE'].str.split().str.get(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "t_df['TRANS_DATE'] = t_df['TRANS_DATE'].str.split('/').str.get(0) + '-' + t_df['TRANS_DATE'].str.split('/').str.get(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "41"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# DATA MERGE\n",
    "import gc\n",
    "\n",
    "df_com = df_enc[['FIN','length of stay','DRG']].merge(df_icd[['FIN','ICD_CODE']], on='FIN')\n",
    "df_com['FIN'] = df_com['FIN'].astype('object')\n",
    "del df_enc,df_icd\n",
    "gc.enable()\n",
    "gc.collect()\n",
    "df_com = df_com.merge(t_df[['FIN','TOTAL_RRVENUE','TRANS_DATE']], on='FIN')\n",
    "#df_com = pd.concat([df_com,t_df])\n",
    "del t_df\n",
    "gc.enable()\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df_com['TOTAL_RRVENUE'].groupby([df_com['TRANS_DATE'],df_com['ICD_CODE']]).sum().unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "j = x.index\n",
    "k = []\n",
    "l = []\n",
    "for i in range(x.shape[0]):\n",
    "    temp = x.iloc[i].sort_values(ascending=False)\n",
    "    k.append(temp.index[0])\n",
    "    l.append(temp.values[0])\n",
    "    \n",
    "temp_df = pd.DataFrame(index=j)\n",
    "temp_df['ICD_CODE'] = k\n",
    "temp_df['Revenue'] = l\n",
    "temp_df = pd.crosstab(temp_df.index,temp_df['ICD_CODE'],values=temp_df['Revenue'],aggfunc='mean')\n",
    "temp_df.index = pd.to_datetime(temp_df.index)\n",
    "temp_df = temp_df.sort_index()\n",
    "temp_df.index = temp_df.index.astype('str')\n",
    "temp_df.index = pd.Series(temp_df.index).apply(lambda x: str(x)[:7])\n",
    "temp_df.plot(kind='bar',figsize=(15,6),title='Highest Revenue Disease with respect to month')\n",
    "plt.ticklabel_format(style='plain', axis='y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "j = x.index\n",
    "k = []\n",
    "l = []\n",
    "for i in range(x.shape[0]):\n",
    "    temp = x.iloc[i].sort_values(ascending=True)\n",
    "    temp = temp[temp>0]\n",
    "    k.append(temp.index[0])\n",
    "    l.append(temp.values[0])\n",
    "    \n",
    "temp_df = pd.DataFrame(index=j)\n",
    "temp_df['ICD_CODE'] = k\n",
    "temp_df['Revenue'] = l\n",
    "#temp_df = pd.crosstab(temp_df.index,temp_df['ICD_CODE'],values=temp_df['Revenue'],aggfunc='mean')\n",
    "temp_df.index = pd.to_datetime(temp_df.index)\n",
    "temp_df = temp_df.sort_index()\n",
    "temp_df.index = temp_df.index.astype('str')\n",
    "temp_df.index = pd.Series(temp_df.index).apply(lambda x: str(x)[:7]) \n",
    "temp_df.index = temp_df.index + ' -- ' + temp_df['ICD_CODE']\n",
    "temp_df['Revenue'].plot(kind='bar',figsize=(15,6),title='Lowest Revenue Disease with respect to month')\n",
    "plt.ticklabel_format(style='plain', axis='y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14396"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "del x, temp_df\n",
    "gc.enable()\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df_com.drop_duplicates(['FIN'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp = df['length of stay'].groupby([df['TRANS_DATE']]).sum()\n",
    "temp = temp.sort_index()\n",
    "temp.index = pd.to_datetime(temp.index)\n",
    "temp = temp.sort_index()\n",
    "temp.index = temp.index.astype('str')\n",
    "temp.index = pd.Series(temp.index).apply(lambda x: str(x)[:7]) \n",
    "temp = pd.Series(temp)\n",
    "temp = temp.astype('str')\n",
    "temp = temp.str.split().str.get(0)\n",
    "temp = temp.astype('int')\n",
    "temp.plot(kind='bar',figsize=(15,6),title='Length Of Stay with respect to month')\n",
    "plt.ylabel('Total Number of Days')\n",
    "plt.ticklabel_format(style='plain', axis='y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6138"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "del df\n",
    "gc.enable()\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_com['Less than 1 Day'] = df_com['length of stay']<pd.to_timedelta('1 days')\n",
    "df_com['Less than 10 Days'] = df_com['length of stay']<pd.to_timedelta('10 days')\n",
    "df_com['Less than 20 Days'] = df_com['length of stay']<pd.to_timedelta('20 days')\n",
    "df_com['Less than 30 Days'] = df_com['length of stay']<pd.to_timedelta('30 days')\n",
    "df_com['Greater than 30 Days'] = df_com['length of stay']>pd.to_timedelta('30 days')\n",
    "\n",
    "temp = df_com['TOTAL_RRVENUE'][df_com['Less than 1 Day']].groupby([df_com['TRANS_DATE'][df_com['Less than 1 Day']]]).sum()\n",
    "temp = pd.DataFrame(temp)\n",
    "temp.columns = ['Less than 1 Day']\n",
    "temp['Less than 10 Days'] = df_com['TOTAL_RRVENUE'][df_com['Less than 10 Days']].groupby([df_com['TRANS_DATE'][df_com['Less than 10 Days']]]).sum()\n",
    "temp['Less than 20 Days'] = df_com['TOTAL_RRVENUE'][df_com['Less than 20 Days']].groupby([df_com['TRANS_DATE'][df_com['Less than 20 Days']]]).sum()\n",
    "temp['Less than 30 Days'] = df_com['TOTAL_RRVENUE'][df_com['Less than 30 Days']].groupby([df_com['TRANS_DATE'][df_com['Less than 30 Days']]]).sum()\n",
    "temp['Greater than 30 Days'] = df_com['TOTAL_RRVENUE'][df_com['Greater than 30 Days']].groupby([df_com['TRANS_DATE'][df_com['Greater than 30 Days']]]).sum()\n",
    "\n",
    "\n",
    "temp.index = pd.to_datetime(temp.index)\n",
    "temp = temp.sort_index()\n",
    "temp.index = temp.index.astype('str')\n",
    "temp.index = pd.Series(temp.index).apply(lambda x: str(x)[:7]) \n",
    "\n",
    "temp.plot(kind='bar',figsize=(20,8),title='Total Revenue with respect to Length Of Stay with respect to month')\n",
    "plt.ticklabel_format(style='plain', axis='y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df_com.drop_duplicates(['FIN'])\n",
    "\n",
    "temp = df['ICD_CODE'][df['Less than 1 Day']].groupby([df['ICD_CODE'][df['Less than 1 Day']]]).count()\n",
    "temp = pd.DataFrame(temp)\n",
    "temp.columns = ['Less than 1 Day']\n",
    "temp['Less than 10 Days'] = df['ICD_CODE'][df['Less than 10 Days']].groupby([df['ICD_CODE'][df['Less than 10 Days']]]).count()\n",
    "temp['Less than 20 Days'] = df['ICD_CODE'][df['Less than 20 Days']].groupby([df['ICD_CODE'][df['Less than 20 Days']]]).count()\n",
    "temp['Less than 30 Days'] = df['ICD_CODE'][df['Less than 30 Days']].groupby([df['ICD_CODE'][df['Less than 30 Days']]]).count()\n",
    "temp['Greater than 30 Days'] = df['ICD_CODE'][df['Greater than 30 Days']].groupby([df['ICD_CODE'][df['Greater than 30 Days']]]).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.ticker import NullFormatter  # useful for `logit` scale\n",
    "plt.figure(figsize=(15,8))\n",
    "plt.subplot(151)\n",
    "temp['Less than 1 Day'].sort_values(ascending=False)[:10].plot(kind='bar',figsize=(15,6))\n",
    "plt.ylabel('Unique Patients')\n",
    "plt.xlabel('')\n",
    "plt.title('Less than 1 Day')\n",
    "plt.subplot(152)\n",
    "temp['Less than 10 Days'].sort_values(ascending=False)[:10].plot(kind='bar',figsize=(15,6))\n",
    "#plt.ylabel('Most Times')\n",
    "plt.title('Less than 10 Days')\n",
    "plt.xlabel('')\n",
    "plt.subplot(153)\n",
    "temp['Less than 20 Days'].sort_values(ascending=False)[:10].plot(kind='bar',figsize=(15,6))\n",
    "#plt.ylabel('Most Times')\n",
    "plt.title('Less than 20 Days')\n",
    "plt.xlabel('')\n",
    "plt.subplot(154)\n",
    "temp['Less than 30 Days'].sort_values(ascending=False)[:10].plot(kind='bar',figsize=(15,6))\n",
    "#plt.ylabel('Most Times')\n",
    "plt.title('Less than 30 Days')\n",
    "plt.xlabel('')\n",
    "plt.subplot(155)\n",
    "temp['Greater than 30 Days'].sort_values(ascending=False)[:10].plot(kind='bar',figsize=(15,6))\n",
    "#plt.ylabel('Most Times')\n",
    "plt.title('Greater than 30 Days')\n",
    "plt.xlabel('')\n",
    "#plt.figure(figsize=(15,8))\n",
    "plt.gca().yaxis.set_minor_formatter(NullFormatter())\n",
    "plt.subplots_adjust(top=0.92, bottom=0.08, left=0.10, right=0.95, hspace=0.25,\n",
    "                    wspace=0.35)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}